Overview

Dataset statistics

Number of variables21
Number of observations17689
Missing cells5287
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory168.0 B

Variable types

Numeric12
Categorical7
Boolean2

Alerts

entry_age is highly overall correlated with years_between_high_school_and_collegeHigh correlation
from_public_school is highly overall correlated with quotaHigh correlation
num_approved_credits is highly overall correlated with num_failed_credits and 3 other fieldsHigh correlation
num_credits is highly overall correlated with num_credits_reference_semester and 1 other fieldsHigh correlation
num_credits_reference_semester is highly overall correlated with num_creditsHigh correlation
num_disciplines is highly overall correlated with num_creditsHigh correlation
num_failed_credits is highly overall correlated with num_approved_credits and 1 other fieldsHigh correlation
num_nonattendance_credits is highly overall correlated with num_approved_credits and 1 other fieldsHigh correlation
quota is highly overall correlated with from_public_schoolHigh correlation
semester_average is highly overall correlated with num_approved_credits and 3 other fieldsHigh correlation
status is highly overall correlated with num_approved_credits and 1 other fieldsHigh correlation
years_between_high_school_and_college is highly overall correlated with entry_ageHigh correlation
race is highly imbalanced (52.6%)Imbalance
marital_status is highly imbalanced (66.9%)Imbalance
has_college_degree is highly imbalanced (91.5%)Imbalance
race has 2521 (14.3%) missing valuesMissing
quota has 1783 (10.1%) missing valuesMissing
from_public_school has 478 (2.7%) missing valuesMissing
years_between_high_school_and_college has 462 (2.6%) missing valuesMissing
years_between_high_school_and_college has 514 (2.9%) zerosZeros
semester_average has 1220 (6.9%) zerosZeros
num_approved_credits has 3165 (17.9%) zerosZeros
num_dispensed_credits has 16089 (91.0%) zerosZeros
num_failed_credits has 10167 (57.5%) zerosZeros
num_nonattendance_credits has 13352 (75.5%) zerosZeros
num_locked_credits has 16767 (94.8%) zerosZeros
num_exams has 8423 (47.6%) zerosZeros

Reproduction

Analysis started2024-06-03 18:28:05.131967
Analysis finished2024-06-03 18:28:58.956030
Duration53.82 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

entry_age
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.3%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean24.893552
Minimum15
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:28:59.156716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17
Q118
median21
Q328
95-th percentile47
Maximum86
Range71
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.7287659
Coefficient of variation (CV)0.39081469
Kurtosis2.3766237
Mean24.893552
Median Absolute Deviation (MAD)3
Skewness1.68629
Sum440118
Variance94.648887
MonotonicityNot monotonic
2024-06-03T18:28:59.511096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 2649
15.0%
19 2216
12.5%
17 2178
12.3%
20 1518
 
8.6%
21 1058
 
6.0%
22 853
 
4.8%
23 636
 
3.6%
24 545
 
3.1%
25 422
 
2.4%
26 421
 
2.4%
Other values (48) 5184
29.3%
ValueCountFrequency (%)
15 1
 
< 0.1%
16 173
 
1.0%
17 2178
12.3%
18 2649
15.0%
19 2216
12.5%
20 1518
8.6%
21 1058
 
6.0%
22 853
 
4.8%
23 636
 
3.6%
24 545
 
3.1%
ValueCountFrequency (%)
86 1
 
< 0.1%
75 1
 
< 0.1%
72 4
 
< 0.1%
69 1
 
< 0.1%
68 4
 
< 0.1%
67 9
0.1%
66 4
 
< 0.1%
65 4
 
< 0.1%
64 5
 
< 0.1%
63 15
0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.3 KiB
Female
10140 
Male
7549 

Length

Max length6
Median length6
Mean length5.1464752
Min length4

Characters and Unicode

Total characters91036
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 10140
57.3%
Male 7549
42.7%

Length

2024-06-03T18:28:59.915293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:00.274247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 10140
57.3%
male 7549
42.7%

Most occurring characters

ValueCountFrequency (%)
e 27829
30.6%
a 17689
19.4%
l 17689
19.4%
F 10140
 
11.1%
m 10140
 
11.1%
M 7549
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73347
80.6%
Uppercase Letter 17689
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27829
37.9%
a 17689
24.1%
l 17689
24.1%
m 10140
 
13.8%
Uppercase Letter
ValueCountFrequency (%)
F 10140
57.3%
M 7549
42.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 91036
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27829
30.6%
a 17689
19.4%
l 17689
19.4%
F 10140
 
11.1%
m 10140
 
11.1%
M 7549
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27829
30.6%
a 17689
19.4%
l 17689
19.4%
F 10140
 
11.1%
m 10140
 
11.1%
M 7549
 
8.3%

race
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing2521
Missing (%)14.3%
Memory size138.3 KiB
White
11287 
Mixed_race
1837 
Black
1408 
Prefer_not_to_declare
 
546
Yellow
 
66

Length

Max length21
Median length5
Mean length6.1937632
Min length5

Characters and Unicode

Total characters93947
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowWhite
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 11287
63.8%
Mixed_race 1837
 
10.4%
Black 1408
 
8.0%
Prefer_not_to_declare 546
 
3.1%
Yellow 66
 
0.4%
Indigenous 24
 
0.1%
(Missing) 2521
 
14.3%

Length

2024-06-03T18:29:00.677596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:01.180223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
white 11287
74.4%
mixed_race 1837
 
12.1%
black 1408
 
9.3%
prefer_not_to_declare 546
 
3.6%
yellow 66
 
0.4%
indigenous 24
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 17235
18.3%
i 13148
14.0%
t 12379
13.2%
W 11287
12.0%
h 11287
12.0%
a 3791
 
4.0%
c 3791
 
4.0%
_ 3475
 
3.7%
r 3475
 
3.7%
d 2407
 
2.6%
Other values (15) 11672
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75304
80.2%
Uppercase Letter 15168
 
16.1%
Connector Punctuation 3475
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17235
22.9%
i 13148
17.5%
t 12379
16.4%
h 11287
15.0%
a 3791
 
5.0%
c 3791
 
5.0%
r 3475
 
4.6%
d 2407
 
3.2%
l 2086
 
2.8%
x 1837
 
2.4%
Other values (8) 3868
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
W 11287
74.4%
M 1837
 
12.1%
B 1408
 
9.3%
P 546
 
3.6%
Y 66
 
0.4%
I 24
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 3475
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90472
96.3%
Common 3475
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17235
19.1%
i 13148
14.5%
t 12379
13.7%
W 11287
12.5%
h 11287
12.5%
a 3791
 
4.2%
c 3791
 
4.2%
r 3475
 
3.8%
d 2407
 
2.7%
l 2086
 
2.3%
Other values (14) 9586
10.6%
Common
ValueCountFrequency (%)
_ 3475
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17235
18.3%
i 13148
14.0%
t 12379
13.2%
W 11287
12.0%
h 11287
12.0%
a 3791
 
4.0%
c 3791
 
4.0%
_ 3475
 
3.7%
r 3475
 
3.7%
d 2407
 
2.6%
Other values (15) 11672
12.4%

marital_status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing34
Missing (%)0.2%
Memory size138.3 KiB
Single
14822 
Married
1929 
Divorced
 
435
Others
 
369
Widowed
 
73

Length

Max length17
Median length6
Mean length6.1794959
Min length6

Characters and Unicode

Total characters109099
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle

Common Values

ValueCountFrequency (%)
Single 14822
83.8%
Married 1929
 
10.9%
Divorced 435
 
2.5%
Others 369
 
2.1%
Widowed 73
 
0.4%
Legally_separated 27
 
0.2%
(Missing) 34
 
0.2%

Length

2024-06-03T18:29:01.641717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:02.119825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
single 14822
84.0%
married 1929
 
10.9%
divorced 435
 
2.5%
others 369
 
2.1%
widowed 73
 
0.4%
legally_separated 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 17709
16.2%
i 17259
15.8%
l 14876
13.6%
g 14849
13.6%
S 14822
13.6%
n 14822
13.6%
r 4689
 
4.3%
d 2537
 
2.3%
a 2010
 
1.8%
M 1929
 
1.8%
Other values (14) 3597
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91417
83.8%
Uppercase Letter 17655
 
16.2%
Connector Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17709
19.4%
i 17259
18.9%
l 14876
16.3%
g 14849
16.2%
n 14822
16.2%
r 4689
 
5.1%
d 2537
 
2.8%
a 2010
 
2.2%
o 508
 
0.6%
v 435
 
0.5%
Other values (7) 1723
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 14822
84.0%
M 1929
 
10.9%
D 435
 
2.5%
O 369
 
2.1%
W 73
 
0.4%
L 27
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109072
> 99.9%
Common 27
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17709
16.2%
i 17259
15.8%
l 14876
13.6%
g 14849
13.6%
S 14822
13.6%
n 14822
13.6%
r 4689
 
4.3%
d 2537
 
2.3%
a 2010
 
1.8%
M 1929
 
1.8%
Other values (13) 3570
 
3.3%
Common
ValueCountFrequency (%)
_ 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17709
16.2%
i 17259
15.8%
l 14876
13.6%
g 14849
13.6%
S 14822
13.6%
n 14822
13.6%
r 4689
 
4.3%
d 2537
 
2.3%
a 2010
 
1.8%
M 1929
 
1.8%
Other values (14) 3597
 
3.3%

quota
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1783
Missing (%)10.1%
Memory size138.3 KiB
OC
9273 
L05
2243 
L01
2172 
L06
1109 
L02
1109 

Length

Max length3
Median length2
Mean length2.4170124
Min length2

Characters and Unicode

Total characters38445
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOC
2nd rowOC
3rd rowOC
4th rowOC
5th rowOC

Common Values

ValueCountFrequency (%)
OC 9273
52.4%
L05 2243
 
12.7%
L01 2172
 
12.3%
L06 1109
 
6.3%
L02 1109
 
6.3%
(Missing) 1783
 
10.1%

Length

2024-06-03T18:29:02.391219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:02.646912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
oc 9273
58.3%
l05 2243
 
14.1%
l01 2172
 
13.7%
l06 1109
 
7.0%
l02 1109
 
7.0%

Most occurring characters

ValueCountFrequency (%)
O 9273
24.1%
C 9273
24.1%
L 6633
17.3%
0 6633
17.3%
5 2243
 
5.8%
1 2172
 
5.6%
6 1109
 
2.9%
2 1109
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25179
65.5%
Decimal Number 13266
34.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6633
50.0%
5 2243
 
16.9%
1 2172
 
16.4%
6 1109
 
8.4%
2 1109
 
8.4%
Uppercase Letter
ValueCountFrequency (%)
O 9273
36.8%
C 9273
36.8%
L 6633
26.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 25179
65.5%
Common 13266
34.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6633
50.0%
5 2243
 
16.9%
1 2172
 
16.4%
6 1109
 
8.4%
2 1109
 
8.4%
Latin
ValueCountFrequency (%)
O 9273
36.8%
C 9273
36.8%
L 6633
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 9273
24.1%
C 9273
24.1%
L 6633
17.3%
0 6633
17.3%
5 2243
 
5.8%
1 2172
 
5.6%
6 1109
 
2.9%
2 1109
 
2.9%

from_public_school
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing478
Missing (%)2.7%
Memory size34.7 KiB
True
12543 
False
4668 
(Missing)
 
478
ValueCountFrequency (%)
True 12543
70.9%
False 4668
 
26.4%
(Missing) 478
 
2.7%
2024-06-03T18:29:02.902332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

has_college_degree
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
False
17501 
True
 
188
ValueCountFrequency (%)
False 17501
98.9%
True 188
 
1.1%
2024-06-03T18:29:03.132101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

shift
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.3 KiB
Morning_Afternoon
8663 
Night
4113 
Full_time
2658 
Afternoon
1544 
Afternoon_Night
 
623

Length

Max length17
Median length15
Mean length12.189214
Min length5

Characters and Unicode

Total characters215615
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMorning_Afternoon
2nd rowMorning_Afternoon
3rd rowMorning_Afternoon
4th rowMorning_Afternoon
5th rowMorning_Afternoon

Common Values

ValueCountFrequency (%)
Morning_Afternoon 8663
49.0%
Night 4113
23.3%
Full_time 2658
 
15.0%
Afternoon 1544
 
8.7%
Afternoon_Night 623
 
3.5%
Morning 88
 
0.5%

Length

2024-06-03T18:29:03.334609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:03.607599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
morning_afternoon 8663
49.0%
night 4113
23.3%
full_time 2658
 
15.0%
afternoon 1544
 
8.7%
afternoon_night 623
 
3.5%
morning 88
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 39162
18.2%
o 30411
14.1%
r 19581
9.1%
t 18224
8.5%
i 16145
7.5%
e 13488
 
6.3%
g 13487
 
6.3%
_ 11944
 
5.5%
f 10830
 
5.0%
A 10830
 
5.0%
Other values (7) 31513
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 176696
81.9%
Uppercase Letter 26975
 
12.5%
Connector Punctuation 11944
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 39162
22.2%
o 30411
17.2%
r 19581
11.1%
t 18224
10.3%
i 16145
9.1%
e 13488
 
7.6%
g 13487
 
7.6%
f 10830
 
6.1%
l 5316
 
3.0%
h 4736
 
2.7%
Other values (2) 5316
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 10830
40.1%
M 8751
32.4%
N 4736
17.6%
F 2658
 
9.9%
Connector Punctuation
ValueCountFrequency (%)
_ 11944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203671
94.5%
Common 11944
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 39162
19.2%
o 30411
14.9%
r 19581
9.6%
t 18224
8.9%
i 16145
7.9%
e 13488
 
6.6%
g 13487
 
6.6%
f 10830
 
5.3%
A 10830
 
5.3%
M 8751
 
4.3%
Other values (6) 22762
11.2%
Common
ValueCountFrequency (%)
_ 11944
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 39162
18.2%
o 30411
14.1%
r 19581
9.1%
t 18224
8.5%
i 16145
7.5%
e 13488
 
6.3%
g 13487
 
6.3%
_ 11944
 
5.5%
f 10830
 
5.0%
A 10830
 
5.0%
Other values (7) 31513
14.6%

years_between_high_school_and_college
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)0.3%
Missing462
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.005979
Minimum0
Maximum78
Zeros514
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:03.885611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q38
95-th percentile22
Maximum78
Range78
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.311533
Coefficient of variation (CV)1.2173757
Kurtosis5.40749
Mean6.005979
Median Absolute Deviation (MAD)2
Skewness2.1586312
Sum103465
Variance53.458515
MonotonicityNot monotonic
2024-06-03T18:29:04.177848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5077
28.7%
2 2363
13.4%
3 1546
 
8.7%
4 1053
 
6.0%
5 870
 
4.9%
6 717
 
4.1%
7 582
 
3.3%
0 514
 
2.9%
8 473
 
2.7%
9 404
 
2.3%
Other values (41) 3628
20.5%
(Missing) 462
 
2.6%
ValueCountFrequency (%)
0 514
 
2.9%
1 5077
28.7%
2 2363
13.4%
3 1546
 
8.7%
4 1053
 
6.0%
5 870
 
4.9%
6 717
 
4.1%
7 582
 
3.3%
8 473
 
2.7%
9 404
 
2.3%
ValueCountFrequency (%)
78 1
 
< 0.1%
49 1
 
< 0.1%
48 2
 
< 0.1%
47 2
 
< 0.1%
46 6
< 0.1%
45 3
 
< 0.1%
44 3
 
< 0.1%
43 4
 
< 0.1%
42 10
0.1%
41 6
< 0.1%

fundamental_area
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.3 KiB
Philosophy_Human
6088 
Exact_Technology
4263 
Health_Biological
3031 
Literature_Arts
2854 
Agricultural
1453 

Length

Max length17
Median length16
Mean length15.68144
Min length12

Characters and Unicode

Total characters277389
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgricultural
2nd rowAgricultural
3rd rowAgricultural
4th rowAgricultural
5th rowAgricultural

Common Values

ValueCountFrequency (%)
Philosophy_Human 6088
34.4%
Exact_Technology 4263
24.1%
Health_Biological 3031
17.1%
Literature_Arts 2854
16.1%
Agricultural 1453
 
8.2%

Length

2024-06-03T18:29:04.465781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:04.741271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
philosophy_human 6088
34.4%
exact_technology 4263
24.1%
health_biological 3031
17.1%
literature_arts 2854
16.1%
agricultural 1453
 
8.2%

Most occurring characters

ValueCountFrequency (%)
o 26764
 
9.6%
l 22350
 
8.1%
a 20720
 
7.5%
h 19470
 
7.0%
t 17309
 
6.2%
i 16457
 
5.9%
_ 16236
 
5.9%
c 13010
 
4.7%
e 13002
 
4.7%
u 11848
 
4.3%
Other values (15) 100223
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 227228
81.9%
Uppercase Letter 33925
 
12.2%
Connector Punctuation 16236
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 26764
11.8%
l 22350
9.8%
a 20720
 
9.1%
h 19470
 
8.6%
t 17309
 
7.6%
i 16457
 
7.2%
c 13010
 
5.7%
e 13002
 
5.7%
u 11848
 
5.2%
r 11468
 
5.0%
Other values (7) 54830
24.1%
Uppercase Letter
ValueCountFrequency (%)
H 9119
26.9%
P 6088
17.9%
A 4307
12.7%
T 4263
12.6%
E 4263
12.6%
B 3031
 
8.9%
L 2854
 
8.4%
Connector Punctuation
ValueCountFrequency (%)
_ 16236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 261153
94.1%
Common 16236
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 26764
 
10.2%
l 22350
 
8.6%
a 20720
 
7.9%
h 19470
 
7.5%
t 17309
 
6.6%
i 16457
 
6.3%
c 13010
 
5.0%
e 13002
 
5.0%
u 11848
 
4.5%
r 11468
 
4.4%
Other values (14) 88755
34.0%
Common
ValueCountFrequency (%)
_ 16236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277389
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 26764
 
9.6%
l 22350
 
8.1%
a 20720
 
7.5%
h 19470
 
7.0%
t 17309
 
6.2%
i 16457
 
5.9%
_ 16236
 
5.9%
c 13010
 
4.7%
e 13002
 
4.7%
u 11848
 
4.3%
Other values (15) 100223
36.1%

num_disciplines
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3100797
Minimum1
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:05.034625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum66
Range65
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4449556
Coefficient of variation (CV)0.54594486
Kurtosis49.092555
Mean6.3100797
Median Absolute Deviation (MAD)1
Skewness6.1233791
Sum111619
Variance11.867719
MonotonicityNot monotonic
2024-06-03T18:29:05.317885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6901
39.0%
6 5304
30.0%
7 2794
15.8%
4 756
 
4.3%
8 601
 
3.4%
9 191
 
1.1%
3 161
 
0.9%
10 120
 
0.7%
2 110
 
0.6%
11 90
 
0.5%
Other values (40) 661
 
3.7%
ValueCountFrequency (%)
1 26
 
0.1%
2 110
 
0.6%
3 161
 
0.9%
4 756
 
4.3%
5 6901
39.0%
6 5304
30.0%
7 2794
15.8%
8 601
 
3.4%
9 191
 
1.1%
10 120
 
0.7%
ValueCountFrequency (%)
66 1
 
< 0.1%
54 1
 
< 0.1%
48 1
 
< 0.1%
47 2
< 0.1%
46 3
< 0.1%
45 1
 
< 0.1%
44 2
< 0.1%
43 3
< 0.1%
42 3
< 0.1%
41 4
< 0.1%

num_credits
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5357
Minimum2
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:05.596522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q120
median22
Q326
95-th percentile35
Maximum243
Range241
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.06481
Coefficient of variation (CV)0.53248165
Kurtosis49.963833
Mean24.5357
Median Absolute Deviation (MAD)2
Skewness6.0780578
Sum434012
Variance170.68926
MonotonicityNot monotonic
2024-06-03T18:29:05.879752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 5309
30.0%
24 1954
 
11.0%
22 1847
 
10.4%
26 1411
 
8.0%
21 961
 
5.4%
18 784
 
4.4%
23 710
 
4.0%
29 692
 
3.9%
31 486
 
2.7%
35 405
 
2.3%
Other values (148) 3130
17.7%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 5
 
< 0.1%
4 21
 
0.1%
6 12
 
0.1%
7 6
 
< 0.1%
8 92
0.5%
9 12
 
0.1%
10 43
 
0.2%
11 15
 
0.1%
12 204
1.2%
ValueCountFrequency (%)
243 1
< 0.1%
220 1
< 0.1%
216 1
< 0.1%
204 1
< 0.1%
179 1
< 0.1%
178 1
< 0.1%
176 1
< 0.1%
170 1
< 0.1%
169 2
< 0.1%
167 1
< 0.1%

semester_average
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct967
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8709893
Minimum0
Maximum10
Zeros1220
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:06.182023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.96
median7.01
Q38.23
95-th percentile9.17
Maximum10
Range10
Interquartile range (IQR)4.27

Descriptive statistics

Standard deviation3.0134864
Coefficient of variation (CV)0.51328425
Kurtosis-0.69701535
Mean5.8709893
Median Absolute Deviation (MAD)1.57
Skewness-0.82005056
Sum103851.93
Variance9.0811001
MonotonicityNot monotonic
2024-06-03T18:29:06.471489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1220
 
6.9%
0.12 143
 
0.8%
8.1 96
 
0.5%
8 94
 
0.5%
8.2 93
 
0.5%
8.4 92
 
0.5%
8.6 92
 
0.5%
8.5 89
 
0.5%
8.3 88
 
0.5%
7.7 81
 
0.5%
Other values (957) 15601
88.2%
ValueCountFrequency (%)
0 1220
6.9%
0.02 13
 
0.1%
0.03 2
 
< 0.1%
0.04 10
 
0.1%
0.05 12
 
0.1%
0.06 13
 
0.1%
0.07 11
 
0.1%
0.08 13
 
0.1%
0.1 20
 
0.1%
0.11 3
 
< 0.1%
ValueCountFrequency (%)
10 12
0.1%
9.94 2
 
< 0.1%
9.93 1
 
< 0.1%
9.9 5
< 0.1%
9.88 1
 
< 0.1%
9.87 1
 
< 0.1%
9.85 4
 
< 0.1%
9.84 1
 
< 0.1%
9.83 2
 
< 0.1%
9.82 5
< 0.1%

num_approved_credits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680536
Minimum0
Maximum60
Zeros3165
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:06.740308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q321
95-th percentile29
Maximum60
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.3807463
Coefficient of variation (CV)0.63899209
Kurtosis-0.9604064
Mean14.680536
Median Absolute Deviation (MAD)6
Skewness-0.28079946
Sum259684
Variance87.998401
MonotonicityNot monotonic
2024-06-03T18:29:06.995175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 3165
17.9%
20 3085
17.4%
16 1128
 
6.4%
22 1107
 
6.3%
24 970
 
5.5%
12 826
 
4.7%
18 700
 
4.0%
8 668
 
3.8%
4 647
 
3.7%
14 552
 
3.1%
Other values (31) 4841
27.4%
ValueCountFrequency (%)
0 3165
17.9%
1 15
 
0.1%
2 238
 
1.3%
3 82
 
0.5%
4 647
 
3.7%
5 52
 
0.3%
6 275
 
1.6%
7 72
 
0.4%
8 668
 
3.8%
9 118
 
0.7%
ValueCountFrequency (%)
60 1
 
< 0.1%
43 1
 
< 0.1%
41 1
 
< 0.1%
38 1
 
< 0.1%
36 3
 
< 0.1%
35 175
1.0%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 11
 
0.1%
31 333
1.9%

num_dispensed_credits
Real number (ℝ)

ZEROS 

Distinct145
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9328962
Minimum0
Maximum231
Zeros16089
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:07.295266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum231
Range231
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.681898
Coefficient of variation (CV)4.6649787
Kurtosis54.258435
Mean2.9328962
Median Absolute Deviation (MAD)0
Skewness6.6923553
Sum51880
Variance187.19433
MonotonicityNot monotonic
2024-06-03T18:29:07.568308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16089
91.0%
4 178
 
1.0%
8 122
 
0.7%
12 109
 
0.6%
16 57
 
0.3%
24 49
 
0.3%
10 42
 
0.2%
20 42
 
0.2%
6 41
 
0.2%
14 39
 
0.2%
Other values (135) 921
 
5.2%
ValueCountFrequency (%)
0 16089
91.0%
2 33
 
0.2%
3 15
 
0.1%
4 178
 
1.0%
5 11
 
0.1%
6 41
 
0.2%
7 18
 
0.1%
8 122
 
0.7%
9 11
 
0.1%
10 42
 
0.2%
ValueCountFrequency (%)
231 1
< 0.1%
204 1
< 0.1%
202 1
< 0.1%
200 1
< 0.1%
162 1
< 0.1%
159 1
< 0.1%
157 1
< 0.1%
156 1
< 0.1%
155 1
< 0.1%
152 1
< 0.1%

num_failed_credits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8878964
Minimum0
Maximum40
Zeros10167
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:07.822743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9485236
Coefficient of variation (CV)1.5300108
Kurtosis1.8752018
Mean3.8878964
Median Absolute Deviation (MAD)0
Skewness1.632099
Sum68773
Variance35.384933
MonotonicityNot monotonic
2024-06-03T18:29:08.110862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 10167
57.5%
4 2329
 
13.2%
8 1056
 
6.0%
20 714
 
4.0%
12 535
 
3.0%
10 439
 
2.5%
6 420
 
2.4%
16 336
 
1.9%
2 267
 
1.5%
14 248
 
1.4%
Other values (23) 1178
 
6.7%
ValueCountFrequency (%)
0 10167
57.5%
2 267
 
1.5%
3 199
 
1.1%
4 2329
 
13.2%
5 159
 
0.9%
6 420
 
2.4%
7 139
 
0.8%
8 1056
 
6.0%
9 68
 
0.4%
10 439
 
2.5%
ValueCountFrequency (%)
40 2
 
< 0.1%
36 1
 
< 0.1%
32 4
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 3
 
< 0.1%
27 6
 
< 0.1%
26 8
 
< 0.1%
25 5
 
< 0.1%
24 35
0.2%

num_nonattendance_credits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6261518
Minimum0
Maximum35
Zeros13352
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:08.373600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7505567
Coefficient of variation (CV)2.1897274
Kurtosis5.6960311
Mean2.6261518
Median Absolute Deviation (MAD)0
Skewness2.4629794
Sum46454
Variance33.068902
MonotonicityNot monotonic
2024-06-03T18:29:08.623342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 13352
75.5%
4 1101
 
6.2%
8 481
 
2.7%
12 333
 
1.9%
16 292
 
1.7%
10 241
 
1.4%
6 238
 
1.3%
18 225
 
1.3%
20 211
 
1.2%
14 211
 
1.2%
Other values (23) 1004
 
5.7%
ValueCountFrequency (%)
0 13352
75.5%
1 1
 
< 0.1%
2 166
 
0.9%
3 79
 
0.4%
4 1101
 
6.2%
5 75
 
0.4%
6 238
 
1.3%
7 50
 
0.3%
8 481
 
2.7%
9 35
 
0.2%
ValueCountFrequency (%)
35 16
 
0.1%
31 21
 
0.1%
30 20
 
0.1%
29 17
 
0.1%
28 4
 
< 0.1%
27 2
 
< 0.1%
26 61
0.3%
25 31
0.2%
24 38
0.2%
23 29
0.2%

num_locked_credits
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4082198
Minimum0
Maximum29
Zeros16767
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:08.875583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0591277
Coefficient of variation (CV)5.0441643
Kurtosis44.342248
Mean0.4082198
Median Absolute Deviation (MAD)0
Skewness6.2475523
Sum7221
Variance4.2400071
MonotonicityNot monotonic
2024-06-03T18:29:09.129780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 16767
94.8%
4 307
 
1.7%
8 122
 
0.7%
12 77
 
0.4%
6 74
 
0.4%
10 57
 
0.3%
2 50
 
0.3%
14 39
 
0.2%
3 29
 
0.2%
11 26
 
0.1%
Other values (15) 141
 
0.8%
ValueCountFrequency (%)
0 16767
94.8%
2 50
 
0.3%
3 29
 
0.2%
4 307
 
1.7%
5 11
 
0.1%
6 74
 
0.4%
7 20
 
0.1%
8 122
 
0.7%
9 10
 
0.1%
10 57
 
0.3%
ValueCountFrequency (%)
29 1
 
< 0.1%
25 1
 
< 0.1%
24 3
 
< 0.1%
22 4
 
< 0.1%
21 5
 
< 0.1%
20 15
0.1%
19 10
0.1%
18 19
0.1%
17 1
 
< 0.1%
16 22
0.1%

num_credits_reference_semester
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.736955
Minimum12
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:09.368868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18
Q120
median22
Q326
95-th percentile35
Maximum40
Range28
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.2316264
Coefficient of variation (CV)0.22040006
Kurtosis0.80521125
Mean23.736955
Median Absolute Deviation (MAD)2
Skewness1.0476569
Sum419883
Variance27.369915
MonotonicityNot monotonic
2024-06-03T18:29:09.573600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20 5619
31.8%
24 2229
 
12.6%
22 1720
 
9.7%
26 1289
 
7.3%
21 877
 
5.0%
29 774
 
4.4%
18 562
 
3.2%
28 537
 
3.0%
36 517
 
2.9%
23 506
 
2.9%
Other values (10) 3059
17.3%
ValueCountFrequency (%)
12 103
 
0.6%
14 160
 
0.9%
16 387
 
2.2%
18 562
 
3.2%
20 5619
31.8%
21 877
 
5.0%
22 1720
 
9.7%
23 506
 
2.9%
24 2229
 
12.6%
25 498
 
2.8%
ValueCountFrequency (%)
40 246
 
1.4%
36 517
2.9%
35 454
 
2.6%
34 472
 
2.7%
31 418
 
2.4%
30 289
 
1.6%
29 774
4.4%
28 537
3.0%
27 32
 
0.2%
26 1289
7.3%

num_exams
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0134547
Minimum0
Maximum6
Zeros8423
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size138.3 KiB
2024-06-03T18:29:09.777889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2278521
Coefficient of variation (CV)1.2115511
Kurtosis0.60308495
Mean1.0134547
Median Absolute Deviation (MAD)1
Skewness1.138134
Sum17927
Variance1.5076209
MonotonicityNot monotonic
2024-06-03T18:29:09.966049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 8423
47.6%
1 4097
23.2%
2 2742
 
15.5%
3 1578
 
8.9%
4 653
 
3.7%
5 176
 
1.0%
6 20
 
0.1%
ValueCountFrequency (%)
0 8423
47.6%
1 4097
23.2%
2 2742
 
15.5%
3 1578
 
8.9%
4 653
 
3.7%
5 176
 
1.0%
6 20
 
0.1%
ValueCountFrequency (%)
6 20
 
0.1%
5 176
 
1.0%
4 653
 
3.7%
3 1578
 
8.9%
2 2742
 
15.5%
1 4097
23.2%
0 8423
47.6%

status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.3 KiB
Dropout
10664 
Graduated
7025 

Length

Max length9
Median length7
Mean length7.7942789
Min length7

Characters and Unicode

Total characters137873
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduated
2nd rowGraduated
3rd rowGraduated
4th rowGraduated
5th rowGraduated

Common Values

ValueCountFrequency (%)
Dropout 10664
60.3%
Graduated 7025
39.7%

Length

2024-06-03T18:29:10.218323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-03T18:29:10.483824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dropout 10664
60.3%
graduated 7025
39.7%

Most occurring characters

ValueCountFrequency (%)
o 21328
15.5%
r 17689
12.8%
u 17689
12.8%
t 17689
12.8%
a 14050
10.2%
d 14050
10.2%
D 10664
7.7%
p 10664
7.7%
G 7025
 
5.1%
e 7025
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120184
87.2%
Uppercase Letter 17689
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 21328
17.7%
r 17689
14.7%
u 17689
14.7%
t 17689
14.7%
a 14050
11.7%
d 14050
11.7%
p 10664
8.9%
e 7025
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
D 10664
60.3%
G 7025
39.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 137873
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 21328
15.5%
r 17689
12.8%
u 17689
12.8%
t 17689
12.8%
a 14050
10.2%
d 14050
10.2%
D 10664
7.7%
p 10664
7.7%
G 7025
 
5.1%
e 7025
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 21328
15.5%
r 17689
12.8%
u 17689
12.8%
t 17689
12.8%
a 14050
10.2%
d 14050
10.2%
D 10664
7.7%
p 10664
7.7%
G 7025
 
5.1%
e 7025
 
5.1%

Interactions

2024-06-03T18:28:53.166490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:15.977081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:18.865062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:22.626578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:26.260936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:30.359718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:33.496802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:36.419064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:39.263196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:43.645140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:47.372063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:50.296058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:53.422317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:16.215228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:19.114520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:22.861991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:26.621480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:30.608448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:33.739729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:36.658824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:39.507044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:43.998244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:47.618793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:50.533803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:53.673480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:16.466624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:19.368407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:23.136343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:26.977171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:30.859726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:33.993895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:36.909001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:39.771913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:44.365628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:47.875073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:50.784390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:53.906578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:16.702765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:19.616111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:23.376012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:27.340692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:31.345164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:34.235757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:37.153012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:40.015560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:44.754988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:48.116174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:51.020942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:54.144313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:16.949773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:19.853900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:23.606258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:27.680149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:31.584796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:34.462899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:37.371942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:40.345839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:45.127364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:48.366444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:51.256757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:54.397277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:17.185973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:20.698358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:23.839582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:28.019332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:31.808746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:34.710459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:37.600277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:40.699757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:45.454150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:48.601179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:51.499403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:54.645227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:17.422527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:20.952201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:24.194575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:28.342994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:32.051869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:34.946464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:37.846486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:41.087789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:45.809736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:48.839606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:51.734914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:54.863224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:17.646529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:21.361936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:24.563882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:28.696257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:32.281289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:35.178255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:38.066147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:41.391448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:46.138946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:49.068115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:51.961869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:55.112622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:17.899193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:21.624563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:24.902408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:29.085784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:32.545493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:35.429232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:38.310837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:41.790275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:46.394488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:49.329937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:52.210546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:55.358941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:18.150687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:21.873578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:25.287981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:29.477391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:32.784924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:35.685749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:38.553967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:42.563099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:46.640796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:49.575296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:52.462652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:55.613022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:18.383561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:22.131149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:25.584754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:29.805553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:33.026739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:35.924898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:38.802321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:42.893898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:46.886917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:49.811785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:52.699893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:56.282481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:18.613226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:22.376830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:25.916831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:30.126263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:33.256170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:36.171288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:39.027873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:43.259050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:47.137574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:50.042423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-03T18:28:52.922904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-03T18:29:10.689699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
entry_agefrom_public_schoolfundamental_areagenderhas_college_degreemarital_statusnum_approved_creditsnum_creditsnum_credits_reference_semesternum_disciplinesnum_dispensed_creditsnum_examsnum_failed_creditsnum_locked_creditsnum_nonattendance_creditsquotaracesemester_averageshiftstatusyears_between_high_school_and_college
entry_age1.0000.0500.1960.0530.1200.303-0.243-0.156-0.177-0.1370.155-0.0810.0760.0340.0450.1040.049-0.1310.2430.1630.839
from_public_school0.0501.0000.0480.0580.0000.022-0.051-0.055-0.017-0.045-0.0440.0410.0490.0190.0170.5170.173-0.0480.0750.0160.012
fundamental_area0.1960.0481.0000.1380.0530.130-0.092-0.358-0.426-0.254-0.030-0.155-0.064-0.032-0.0800.0630.0330.0800.4200.2510.299
gender0.0530.0580.1381.0000.0000.050-0.063-0.058-0.0830.028-0.0000.0070.018-0.0210.0610.0490.047-0.0420.1140.088-0.015
has_college_degree0.1200.0000.0530.0001.0000.071-0.0450.012-0.0330.0120.063-0.0330.0090.0030.0270.0250.037-0.0270.0740.0510.094
marital_status0.3030.0220.1300.0500.0711.0000.0940.1240.1120.128-0.0090.031-0.0380.0070.0340.0650.0090.0390.1690.074-0.409
num_approved_credits-0.243-0.051-0.092-0.063-0.0450.0941.0000.3240.3240.215-0.1170.099-0.616-0.160-0.6000.0450.0280.7470.1550.525-0.169
num_credits-0.156-0.055-0.358-0.0580.0120.1240.3241.0000.6570.6890.3710.104-0.0190.0800.0100.0250.0380.0480.1230.111-0.095
num_credits_reference_semester-0.177-0.017-0.426-0.083-0.0330.1120.3240.6571.0000.4230.0410.093-0.0370.033-0.0020.0580.0210.0540.3860.214-0.128
num_disciplines-0.137-0.045-0.2540.0280.0120.1280.2150.6890.4231.0000.3870.032-0.0860.1020.0260.0220.0400.1380.0720.058-0.077
num_dispensed_credits0.155-0.044-0.030-0.0000.063-0.009-0.1170.3710.0410.3871.000-0.105-0.1090.036-0.0120.0330.0450.0670.0430.0400.201
num_exams-0.0810.041-0.1550.007-0.0330.0310.0990.1040.0930.032-0.1051.0000.302-0.074-0.1950.0490.040-0.2090.0790.021-0.088
num_failed_credits0.0760.049-0.0640.0180.009-0.038-0.616-0.019-0.037-0.086-0.1090.3021.000-0.0150.2180.0570.026-0.7000.1920.3520.021
num_locked_credits0.0340.019-0.032-0.0210.0030.007-0.1600.0800.0330.1020.036-0.074-0.0151.0000.1030.0060.020-0.0650.0560.1040.028
num_nonattendance_credits0.0450.017-0.0800.0610.0270.034-0.6000.010-0.0020.026-0.012-0.1950.2180.1031.0000.0150.024-0.6320.1070.3690.005
quota0.1040.5170.0630.0490.0250.0650.0450.0250.0580.0220.0330.0490.0570.0060.0151.0000.384-0.0180.1310.0790.187
race0.0490.1730.0330.0470.0370.0090.0280.0380.0210.0400.0450.0400.0260.0200.0240.3841.0000.0520.0500.041-0.019
semester_average-0.131-0.0480.080-0.042-0.0270.0390.7470.0480.0540.1380.067-0.209-0.700-0.065-0.632-0.0180.0521.0000.1170.516-0.059
shift0.2430.0750.4200.1140.0740.1690.1550.1230.3860.0720.0430.0790.1920.0560.1070.1310.0500.1171.0000.137-0.131
status0.1630.0160.2510.0880.0510.0740.5250.1110.2140.0580.0400.0210.3520.1040.3690.0790.0410.5160.1371.000-0.093
years_between_high_school_and_college0.8390.0120.299-0.0150.094-0.409-0.169-0.095-0.128-0.0770.201-0.0880.0210.0280.0050.187-0.019-0.059-0.131-0.0931.000

Missing values

2024-06-03T18:28:56.820386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-03T18:28:57.879713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-03T18:28:58.624050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

entry_agegenderracemarital_statusquotafrom_public_schoolhas_college_degreeshiftyears_between_high_school_and_collegefundamental_areanum_disciplinesnum_creditssemester_averagenum_approved_creditsnum_dispensed_creditsnum_failed_creditsnum_nonattendance_creditsnum_locked_creditsnum_credits_reference_semesternum_examsstatus
029.0MaleWhiteSingleOCNoNoMorning_Afternoon12.0Agricultural7247.47240000282Graduated
117.0MaleWhiteSingleOCNoNoMorning_Afternoon1.0Agricultural7248.26240000240Graduated
221.0FemaleWhiteSingleOCNoNoMorning_Afternoon5.0Agricultural6228.18220000240Graduated
321.0MaleWhiteSingleOCNoNoMorning_Afternoon4.0Agricultural7248.14240000241Graduated
417.0MaleWhiteSingleOCNoNoMorning_Afternoon1.0Agricultural7248.74240000240Graduated
521.0MaleWhiteSingleOCYesNoMorning_Afternoon3.0Agricultural6226.48200200241Dropout
620.0FemalePrefer_not_to_declareSingleOCYesNoMorning_Afternoon5.0Agricultural7247.99240000240Graduated
719.0FemaleWhiteSingleOCYesNoMorning_Afternoon1.0Agricultural6228.72220000240Graduated
820.0MaleMixed_raceSingleL06YesNoMorning_Afternoon3.0Agricultural7242.46481200241Dropout
918.0FemaleBlackSingleL06YesNoMorning_Afternoon2.0Agricultural7240.00002148240Dropout
entry_agegenderracemarital_statusquotafrom_public_schoolhas_college_degreeshiftyears_between_high_school_and_collegefundamental_areanum_disciplinesnum_creditssemester_averagenum_approved_creditsnum_dispensed_creditsnum_failed_creditsnum_nonattendance_creditsnum_locked_creditsnum_credits_reference_semesternum_examsstatus
1767922.0MaleWhiteSingleNaNYesNoFull_time3.0Literature_Arts271.0000700230Dropout
1768019.0MaleWhiteSingleNaNYesNoFull_time2.0Literature_Arts6230.15002300230Dropout
1768120.0FemaleWhiteSingleNaNYesNoFull_time1.0Literature_Arts5196.64190000230Dropout
1768251.0FemaleWhiteMarriedNaNYesNoFull_time18.0Literature_Arts6230.00002300230Dropout
1768319.0MaleWhiteSingleNaNNaNNoFull_timeNaNPhilosophy_Human147.0040000200Dropout
1768420.0FemaleWhiteSingleL01YesNoMorning_Afternoon2.0Health_Biological6298.93290000290Graduated
1768519.0MaleWhiteSingleOCYesNoMorning_Afternoon2.0Health_Biological6298.80290000290Graduated
1768633.0FemaleWhiteSingleNaNNoNoFull_time14.0Philosophy_Human5202.44401600202Dropout
1768725.0MaleBlackSingleNaNYesNoMorning_Afternoon2.0Health_Biological5213.62901200214Dropout
1768818.0FemaleMixed_raceSingleNaNYesNoNight1.0Philosophy_Human7247.41220200281Graduated